[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118323-en":3,"doc-seo-118323-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118323,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine-learning-enabled Decision Support for Screwdriving Process","Screwdriving is a typical production process facing massive data streams from sensors, machines, and production lines. Turning this abundance into knowledge requires close human–machine cooperation and practical decision support. The work integrates big data analysis, machine learning, and AI within Cyber-Physical Production Systems to reduce the expertise needed for interpretation. A data analysis and machine-learning toolbox is developed under a CPPS framework and validated on an industrial-scale learning-factory screwdriving case study, where a random forest model identifies screwing conditions with high accuracy.","2024 IEEE 22nd International Conference on Industrial Informatics (INDIN) ©2024 IEEE DOI: 10.1109/INDIN58382.2024.10774335| 979-8-3315-2747-1/24/$31.00 |   \nMachine-learning-enabled Decision Support for  \nScrewdriving Process  \n1st Zhenkai Yang, 2nd Poorya Ghafoorpoor Yazdi, 3rd Sebastian Thiede Chair of Manufacturing Systems, Department of Design, Production and Management  \nUniversity of Twente  \nEnschede, Netherlands  \n{z.yang-4, p.ghafoorpooryazdi, [s.thiede](s.thiede}@utwente.nl)[}](s.thiede}@utwente.nl)[@utwente.nl](s.thiede}@utwente.nl)  \nAbstract—Screwdriving as one of the most typical processes in production is facing massive amounts of data. These data empower engineers, managers, and manufacturers to understand the screwing process and make decisions. However, the prerequisites are the ability to turn the abundance of data into knowledge and the intensive cooperation between humans and machines. Nowadays, technologies like big data analysis, machine learning (ML), and artificial intelligence (AI) can be integrated into CyberPhysical Production Systems (CPPS). They help to get a better insight into the manufacturing processes. However, utilizing these techniques can require human efforts and expertise. In this paper, we show that machine learning can support decision-making in the screwdriving process. The proposed approach is validatedon an industrial-scale screwdriving case study in a learning factory. Under the CPPS framework, a data analysis and machine learning toolbox is developed. Our test results show that this toolbox can help users understand the screwdriving process with less expert knowledge. The random forest algorithm as the best fit could effectively identify the screwing condition with an accuracy of 0.93 and F1 score of 0.90.  \nIndex Terms—Cyber-Physical Production System, Decisionmaking, Machine Learning, Screwdriving Process  \nI. INTRODUCTION  \nCurrent Cyber-Physical Production Systems (CPPS) are facing massive amounts of data. These data come from sensors, machines, and production lines. They empower engineers, managers, and manufacturers to understand manufacturing systems and make decisions based on that. However, the prerequisites are the ability to turn the data into actionable conclusions and the intensive cooperation between humans and machines. In other words, the ongoing transformation of input data streams into actionable decision support, with a focus on human interaction, is essential for constructing the Cyber World. (Fig. 1) .  \nFortunately, with technologies like (big) data analysis, machine learning (ML) and artificial intelligence (AI), CPPS have been advancing to get a better insight into manufacturing systems. Recent researches have integrated these techniques into CPPS to support autonomous control [2]–[4], production planning & scheduling [5]–[7], and condition monitoring and diagnosis [8]–[10] . However, given the fact that the variety of  \nThis project received a contribution from the Growthfund programme NXTGEN Hightech.  \nCorresponding author: Zhenkai Yang [z.yang-4@utwente.nl](z.yang-4@utwente.nl)  \nFig. 1: General framework of CPPS [1]  \nmanufacturing data is diverse and growing rapidly, utilizing these technologies can require human efforts and expertise.  \nIn this research, we tackled the above issues by introducing a machine-learning-enabled approach to support decisionmaking in CPPS. Within this approach, we combine data analysis and machine learning to help users understand manufacturing data and make decisions easier. Specifically, interactive data visualizations and ML algorithms are jointly helping to outline the underlying trends, structures, and correlations, as well as make predictions. The proposed approach is validatedon an industrial-scale screwdriving case study. A toolbox is developed in a learning factory to facilitate a drone production process. Test results show that the toolbox depicts the patterns and correlations of the screwdriving processes. It pre","cbCaivmGpHgHHDnn","https://ap.wps.com/l/cbCaivmGpHgHHDnn","pdf",3482890,1,6,"English","en",105,"# Introduction\n## Technical background and research demand\n### Data analysis in CPPS\n### Data pre-processing\n### Data exploration (EDA)","[{\"question\":\"How does the proposed approach support decision-making in the screwdriving process?\",\"answer\":\"It combines data analysis with machine learning to help users understand manufacturing data through interactive visualizations and prediction using ML algorithms.\"},{\"question\":\"Where is the approach validated, and what case is used?\",\"answer\":\"Validation uses an industrial-scale screwdriving case study in a learning factory, developed within a CPPS framework for a drone production process.\"},{\"question\":\"Which machine learning model performs best, and what results are reported?\",\"answer\":\"Random forest is reported as the best fit, achieving 0.93 accuracy and 0.90 F1 score for identifying screwing conditions.\"}]","Machine-learning-enabled Decision Support for Screwdriving Process | PDF",1785683060,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-enabled-decision-support-for-screwdriving-process","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-enabled-decision-support-for-screwdriving-process/118323/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the proposed approach support decision-making in the screwdriving process?","Question",{"text":75,"@type":76},"It combines data analysis with machine learning to help users understand manufacturing data through interactive visualizations and prediction using ML algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Where is the approach validated, and what case is used?",{"text":80,"@type":76},"Validation uses an industrial-scale screwdriving case study in a learning factory, developed within a CPPS framework for a drone production process.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best, and what results are reported?",{"text":84,"@type":76},"Random forest is reported as the best fit, achieving 0.93 accuracy and 0.90 F1 score for identifying screwing conditions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]